Data Visualization in R‍💻

First published in 1999

A theoretical deconstruction of data graphics

Foundation for many graphic applications

The Grammar of Graphics can be applied to every type of plot

Concisely describe components

Your dataset

Tidy format

There is no visualization without a dataset

Aesthetics mapping: links variable in the data to graphical properties in the geometry.

We can specify the following properties within the aestetic mapping (colour, shape, alpha, fill, size).

Transform input variables to displayed values:

• Bins for histogram

• Summary statistics for boxplot using stat_boxplot()

• No. of observations in a category for bar chart stat_count

Even tidy data may need some transformation

The statistics is linked to the geometry

Scales help us to control the mapping from data to aesthetics

Scales also provide the tools that let you interpret the plot: the axes and legends.

Scales are automatically generated in ggplot and can be customized

• log scale

• We can also specify limit within the scale

Scales help you interpret the plot

Geometries help us to interpret the aesthetics as graphical representation

Determines your plot type

• bar chart geom_bar()
• scatter geom_point()
• box plot geom_boxplot()
• histogram geom_histogram()

Divide your data into panels using one or two groups

Allows you to look at smaller subsets of data

A coordinate system, maps the position of objects onto the plane of the plot.

It is also the physical mapping of the aesthetics to the paper

Coordinate systems affect all position variables simultaneously and differ from scales in that they also change the appearance of the geometric objects.

Coordinate systems control how the axes and grid lines are drawn.

This controls the overall look of the plot

Spans every part of the graphic that is not linked to the data

Themes give you control over things like fonts, ticks, panel strips, and backgrounds